Characterization and natural history of different phenotypes in hereditary transthyretin amyloidosis: 40-year experience at a single Italian referral center

- Aims: Hereditary Transthyretin Amyloidosis (ATTRv) is one of the leading etiologies of systemic amyloidosis with more than 135 mutations described and a broad spectrum of clinical manifestations. We aimed to provide a systematic description of a population of individuals carrying pathogenic mutations of transthyretin (TTR) gene and to investigate the major clinical events during follow up. - Methods: Observational, retrospective, cohort study including consecutive patients with mutations of TTR gene, admitted to a tertiary referral center in Bologna, Italy, between 1984 and 2022. - Results: Three hundred twenty-five patients were included: 106 asymptomatic carriers, 49 cardiac phenotype, 49 neurological phenotype and 121 mixed phenotype. Twenty-three different mutations were found, with Ile68Leu (41.8%), Val30Met (19%), and Glu89Gln (10%) being the most common. After a median follow-up of 51 months data from 290 subjects were analyzed; among them 111 (38.3%) died and 123 (42.4%) had a major clinical event (death or hospitalization for heart failure). Nine (11.5%) of the 78 asymptomatic carriers showed signs and symptoms of the disease. Carriers had a prognosis comparable to healthy population, while no significant differences were seen among the three phenotypes adjusted by age. Age at diagnosis, NYHA functional class, left ventricular ejection fraction, mPND score and disease-modifying therapy were independently associated with survival. - Conclusions: This study offers a wide and comprehensive overview of ATTRv from the point of view of a tertiary referral center in Italy. Three main phenotypes can be identified (cardiac, neurological and mixed) with specific clinical and instrumental features. Family screening programs are essential to identify paucisymptomatic affected patients or unaffected carriers of the mutation, to be followed through the years. Lastly, disease-modifying therapy represents an evolving cornerstone of the management of ATTRv, with a great impact on mortality.

A partial dependence factorial analysis to deal with selection bias in observational studis

This thesis presents a creative and practical approach to dealing with the problem of selection bias. Selection bias may be the most important vexing problem in program evaluation or in any line of research that attempts to assert causality. Some of the greatest minds in economics and statistics have scrutinized the problem of selection bias, with the resulting approaches – Rubin’s Potential Outcome Approach(Rosenbaum and Rubin,1983; Rubin, 1991,2001,2004) or Heckman’s Selection model (Heckman, 1979) – being widely accepted and used as the best fixes. These solutions to the bias that arises in particular from self selection are imperfect, and many researchers, when feasible, reserve their strongest causal inference for data from experimental rather than observational studies. The innovative aspect of this thesis is to propose a data transformation that allows measuring and testing in an automatic and multivariate way the presence of selection bias. The approach involves the construction of a multi-dimensional conditional space of the X matrix in which the bias associated with the treatment assignment has been eliminated. Specifically, we propose the use of a partial dependence analysis of the X-space as a tool for investigating the dependence relationship between a set of observable pre-treatment categorical covariates X and a treatment indicator variable T, in order to obtain a measure of bias according to their dependence structure. The measure of selection bias is then expressed in terms of inertia due to the dependence between X and T that has been eliminated. Given the measure of selection bias, we propose a multivariate test of imbalance in order to check if the detected bias is significant, by using the asymptotical distribution of inertia due to T (Estadella et al. 2005) , and by preserving the multivariate nature of data. Further, we propose the use of a clustering procedure as a tool to find groups of comparable units on which estimate local causal effects, and the use of the multivariate test of imbalance as a stopping rule in choosing the best cluster solution set. The method is non parametric, it does not call for modeling the data, based on some underlying theory or assumption about the selection process, but instead it calls for using the existing variability within the data and letting the data to speak. The idea of proposing this multivariate approach to measure selection bias and test balance comes from the consideration that in applied research all aspects of multivariate balance, not represented in the univariate variable- by-variable summaries, are ignored. The first part contains an introduction to evaluation methods as part of public and private decision process and a review of the literature of evaluation methods. The attention is focused on Rubin Potential Outcome Approach, matching methods, and briefly on Heckman’s Selection Model. The second part focuses on some resulting limitations of conventional methods, with particular attention to the problem of how testing in the correct way balancing. The third part contains the original contribution proposed , a simulation study that allows to check the performance of the method for a given dependence setting and an application to a real data set. Finally, we discuss, conclude and explain our future perspectives.